This course provides an introduction to reinforcement learning intelligence, which focuses on the study and design of agents that interact with a complex, uncertain world to achieve a goal. The course covers Markov decision processes, reinforcement learning, planning, and function approximation (online supervised learning). Applications to computer vision, robotics, etc. are explored, and common RL algorithms are analyzed and implemented.